Schema database

Remember that you are modeling the business, and not writing a database schema or program or modeling purely physical relationships. The associations are attributes drawn in pictures and not lines of communication or physical connections. (The latter would normally be drawn as an action.)... 

There are a few design options for the Data Persistence Layer. Here we use the Data Mapper Pattern (Fowler, 2003b). The reason is that we want to separate the domain layer and the database schema and allow them to evolve independently. 

To support these functions, we have organized our data model as a set of concentric circles. Our data model is inspired from the Snort relational database schema, the IDMEF message format (Curry et al., 2004), and the M2D2 model (Morin et al., 2002). We participated in deploying these tools and developing these models, so they naturally were used as a starting point for our development. However, we believe that event and contextual information are not equivalent and this is not obvious in the three models cited before. Hence, we choose to provide a different representation shown in Figure 2. 

Figure 5 Sample database schema. A database schema shows the types and nature of links between different types of data. Each box represents a table within the database. The rows within that box correspond to the fields of the table. The lines connecting the boxes identify the required relationships amongst the different types of data stored in the different tables.

Database schemas are centrally stored and controlled. Data definitions (schema) are stored in the centralized data dictionary. The user s view(s) of the database is defined and stored in the same data dictionary. Programs are given access to individual data fields, records, sets and areas of the database on a need-to-know basis. The database administrator creates and maintains integrity of the database schemas. The benefits of this approach are ... 

Information structures Database schema Data migration strategy... 

Module relationships (events, timers, handshaking) Database schema File structures System interfaces... 

Batini, C., Lenzerini, M., Navathe, S.B. A comparative analysis of methodologies for database schema integration. ACM Computing Surveys 18(4), 323-364 (1986)... 

ArrayExpress implementation at EBI will run on an Oracle 8i platform however, database schema will be easily portable to other RDBMSs. The supported data import format will be MAML, a MIAME-compliant XML language Images will not be stored inside the database, they will be archived on tapes or direct access media such as CD-R or DVD-R. 

An object model-based query interface to ArrayExpress is being developed. The Web interfaces for predefined types of queries will be provided on top of the general query mechanism. For the database to be used for efficient data mining and interactive visualization, extensive optimization may be required, e.g., by tuning of table indexing or producing a denormalized database schema for some parts of the database. 

Schema matching aims at identifying semantic correspondences between metadata structures or models, such as database schemas, XML message formats, and ontologies. Solving such match problems is a key task in numerous application fields, particularly to support data exchange, schema evolution, and virtually all kinds of data integration. Unfortunately, the typically high degree of semantic heterogeneity reflected in different schemas makes schema matching an inherently complex task. Hence, most current systems still require the manual specification of semantic correspondences, e.g., with the help of a GUI. While such an approach is appropriate for...

Schema evolution is the ability to change deployed schemas, i.e., metadata structures formally describing complex artifacts such as databases, messages, application programs, or workflows. Typical schemas thus include relational database schemas, conceptual ER or UML models, ontologies, XML schemas, software interfaces, and workflow specifications. Obviously, the need for schema evolution occurs very often in order to deal with new or changed requirements, to correct deficiencies in the current schemas, to cope with new insights in a domain, or to migrate to a new platform.

To provide an overview about the current state of the art and recent research results on schema evolution in three areas relational database schemas, XML schemas, and ontologies. For each kind of schema, we outline how and to what degree the introduced requirements are served by existing approaches. 

In Sect. 2, we introduce the main requirements for effective schema and ontology evolution. Sections 3 and 4 deal with the evolution of relational database schemas and of XML schemas, respectively. In Sect. 5, we outline proposed approaches for ontology evolution and conclude in Sect. 6. 

Maule A, Emmerich W, Rosenblum DS (2008) Impact analysis of database schema changes. In Proceedings of international conference on software engineering (ICSE). ACM, NY, pp 451 160... 

Chandra AK, Merlin PM (1977) Optimal implementation of conjunctive queries in relational data bases. In ACM symposium on theory of computing (STOC). ACM, NY, pp 77-90 Curino C, Moon HJ, Zaniolo C (2008) Graceful database schema evolution The PRISM workbench. PVLDB 1(1) 761—772... 

Matching is the process that takes as input two schemas, referred to as the source and the target, and produces a number of matches, aka correspondences, between the elements of these two schemas [Rahm and Bernstein 2001], The term schema is used with the broader sense and includes database schemas [Madhavan et al. 2001], ontologies [Giunchiglia et al. 2009], or generic models [Atzeni and Torlone 1995], A match is defined as a triple (Ss,Et,e), where Ss is a set of elements from the... 

Berlin J, Motro A (2002) Database schema matching using machine learning with feature selection. In CAiSE. Springer, London, pp 452 166... 

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